Level: Intermediate
TCM Section(s):
11.3. Information Management
10.4. Project Historical Database Management
Venue: 2023 AACE International Conference & Expo
Abstract: In the era of modern digitalization and data mining, the accuracy of data processing toward real-time information for project planning and control purposes is crucial to supporting overall project delivery and meeting the business objective. It ensures that any strategic business maneuver can be done with high accuracy and fast decision-making.
The internal as-built project schedule, cost data, and external 3rd party benchmark reports have been the main reference for project planning and control (PP&C) practitioners regarding front-end loading schedule development regardless of capital projects or plant change in PETRONAS. The advancement of information systems and increased maturity of project management systems in project controls, including a huge amount of data collected over the past years, have improved the ability to refine the duration estimation and productivity rate holistically. As data becomes more available and interpretable, an initiative has been executed by developing a standalone application using MS Excel coupled with a Visual Basic macro, followed by proper development using a web-based framework architecture and cloud database. The advancement of information systems and increased maturity of project management systems among the project control, including a huge amount of data collected over the last years internally and externally.
In the previous five years, the involvement of machine learning (ML) and artificial intelligence (AI) in project management and other domain disciplines has contributed to many data interpretation and analysis methods. Hence, the forecasting methodologies have changed dramatically, giving better predictions on project conditions by injecting early warning indicators to support the earliest human intervention and avoid major risks. This paper aims to describe how adopting artificial intelligence will allow it to be expanded and supported effectively throughout the overall project management method and ways of execution specific to the portfolio management office (PMO) and at the directorate level. The improvement of current structured data into a more flexible database structure will allow a holistic way of managing bigger portfolios and typically maximize resource effectiveness.
TCM Section(s):
11.3. Information Management
10.4. Project Historical Database Management
Venue: 2023 AACE International Conference & Expo
Abstract: In the era of modern digitalization and data mining, the accuracy of data processing toward real-time information for project planning and control purposes is crucial to supporting overall project delivery and meeting the business objective. It ensures that any strategic business maneuver can be done with high accuracy and fast decision-making.
The internal as-built project schedule, cost data, and external 3rd party benchmark reports have been the main reference for project planning and control (PP&C) practitioners regarding front-end loading schedule development regardless of capital projects or plant change in PETRONAS. The advancement of information systems and increased maturity of project management systems in project controls, including a huge amount of data collected over the past years, have improved the ability to refine the duration estimation and productivity rate holistically. As data becomes more available and interpretable, an initiative has been executed by developing a standalone application using MS Excel coupled with a Visual Basic macro, followed by proper development using a web-based framework architecture and cloud database. The advancement of information systems and increased maturity of project management systems among the project control, including a huge amount of data collected over the last years internally and externally.
In the previous five years, the involvement of machine learning (ML) and artificial intelligence (AI) in project management and other domain disciplines has contributed to many data interpretation and analysis methods. Hence, the forecasting methodologies have changed dramatically, giving better predictions on project conditions by injecting early warning indicators to support the earliest human intervention and avoid major risks. This paper aims to describe how adopting artificial intelligence will allow it to be expanded and supported effectively throughout the overall project management method and ways of execution specific to the portfolio management office (PMO) and at the directorate level. The improvement of current structured data into a more flexible database structure will allow a holistic way of managing bigger portfolios and typically maximize resource effectiveness.